Data · collection · 2015
A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct
Listed in DataCite
This article describes the use of flexible Bayesian regression models for estimating a partially identified probability function.
Description
Our approach permits efficient sensitivity analysis concerning the posterior impact of priors on the partially identified component of the regression model. The new methodology is illustrated on an important problem where only partially observed data are available—inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.
Supplementary materials for this article are available online.
Links
Where it is published
- Repository landing page figshare.com/collections/A_Bayesian_partial_identification_approach_to_infe… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.c.2111546 ↗
DOI / persistent id · from DataCite
Documentation and papers
- CC-BY creativecommons.org/licenses/by/3.0/us ↗
license · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.c.2111546 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.c.2111546 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Chemical sciences · Earth and related environmental sciences · Mathematics
- From keywords
- Cancer · Genetics · Research, science and technology policy
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.c.2111546 | 13 d ago | JSON v1 |
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|---|---|---|---|
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| concepts[field].fos:biological-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:chemical-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:earth-and-related-environmental-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:mathematics | source · DataCite | connector:datacite@1.0.0 | |
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